Machine Learning Perspectives of Agent-Based Models: Practical Applications to Economic Crises and Pandemics with Python, R, Netlogo and Julia
暫譯: 基於代理的模型之機器學習觀點：使用 Python、R、Netlogo 和 Julia 實踐應用於經濟危機與疫情

Campos, Pedro, Rao, Anand, Margarido, Joaquim

Machine Learning Perspectives of Agent-Based Models: Practical Applications to Economic Crises and Pandemics with Python, R, Netlogo and Julia
  • 出版商: Springer
  • 出版日期: 2025-08-19
  • 售價: $6,160
  • 貴賓價: 9.5 折 $5,852
  • 語言: 英文
  • 頁數: 377
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 3031733533
  • ISBN-13: 9783031733536
  • 相關分類: Python、R 語言、Machine Learning

    • 海外代購書籍(需單獨結帳)

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商品描述

This book provides an overview of agent-based modeling (ABM) and multi-agent systems (MAS), emphasizing their significance in understanding complex economic systems, with a special focus on the emerging properties of heterogeneous agents that cannot be deduced from the characteristics of individual agents. ABM is highlighted as a powerful tool for studying economics, especially in the context of financial crises and pandemics, where traditional models, such as dynamic stochastic general equilibrium (DSGE) models, have proven inadequate.

Containing numerous practical examples and applications with R, Python, Julia and Netlogo, the book explores how learning, particularly machine learning, can be integrated into multi-agent systems to enhance the adaptation and behavior of agents in dynamic environments. It compares different learning approaches, including game theory and artificial intelligence, highlighting the advantages of each in modeling economic phenomena.

商品描述(中文翻譯)

本書提供了代理基礎建模（Agent-Based Modeling, ABM）和多代理系統（Multi-Agent Systems, MAS）的概述，強調它們在理解複雜經濟系統中的重要性，特別關注無法從個別代理特徵推導出的異質代理的出現性質。ABM 被強調為研究經濟學的強大工具，特別是在金融危機和疫情的背景下，傳統模型，如動態隨機一般均衡（Dynamic Stochastic General Equilibrium, DSGE）模型，已被證明不足以應對。

本書包含許多實用的範例和應用，使用 R、Python、Julia 和 Netlogo，探討了如何將學習，特別是機器學習，整合到多代理系統中，以增強代理在動態環境中的適應性和行為。它比較了不同的學習方法，包括博弈論和人工智慧，並突顯了每種方法在建模經濟現象中的優勢。

作者簡介

Anand Rao is a Distinguished Services Professor of Applied Data Science and AI in the Heinz College of Information Systems and Public Policy at Carnegie Mellon University. He received his PhD from the University of Sydney (with a University Postgraduate Research Award-UPRA) in 1988 and an MBA (with Award of Distinction) from Melbourne Business School in 1997. He boasts a 35-year career spanning AI, data, and analytics, serving as PwC's Global AI Leader. His research focuses on operationalizing AI, responsible AI, and agent-based models. Recognized globally, he has received accolades such as the Most Influential Paper Award and distinctions in AI and InsureTech. Prior to joining management consulting, he was the Chief Research Scientist at the Australian Artificial Intelligence Institute, where he built agent-based models and simulation systems and conducted research in the theory and practice of multi-agent systems.

Pedro Campos, PhD in Business Sciences (2008), with a background in Mathematics and Statistics, is Associate Professor of the Faculty of Economics, University of Porto, and conducts his research at LIAAD, the Artificial Intelligence and Decision Analysis Laboratory of INESC TEC. He currently serves as the Director of Methodology Services at Statistics Portugal. He specializes in Statistics, Data Science, Network Mining, and Marketing Research. Some of his research contributions delve into Innovation and Employment, Collaborative Networks, and Data Visualization. He has more than 50 publications, including articles in specialized journals and book chapters, and has edited 3 books. Pedro is also Deputy Director of the ISLP (International Statistical Literacy Project).

Joaquim Margarido, an ISEP (Superior Institute of Engineering of Porto) graduate, holds a master's degree in multi-agent systems. With expertise in IT, he imparts knowledge in programming using Java, Python, C#, SQL, and web technologies. Dedicated to practical solutions, Joaquim has developed software for various companies, addressing common challenges. His commitment to innovative software solutions reflects his extensive training and proficiency in diverse programming languages, contributing to both education and industry.

作者簡介(中文翻譯)

Anand Rao 是卡內基梅隆大學海因茲資訊系統與公共政策學院的應用數據科學與人工智慧傑出服務教授。他於1988年獲得悉尼大學的博士學位（並獲得大學研究生獎-UPRA），並於1997年獲得墨爾本商學院的MBA（並獲得卓越獎）。他擁有35年的職業生涯，專注於人工智慧、數據和分析，曾擔任PwC的全球人工智慧領導者。他的研究重點在於人工智慧的實用化、負責任的人工智慧以及基於代理的模型。他在全球範圍內受到認可，曾獲得最具影響力論文獎及在人工智慧和保險科技領域的多項榮譽。在加入管理諮詢之前，他曾擔任澳大利亞人工智慧研究所的首席研究科學家，負責建立基於代理的模型和模擬系統，並進行多代理系統的理論與實踐研究。

Pedro Campos，擁有商業科學博士學位（2008年），背景為數學和統計學，是波爾圖大學經濟學院的副教授，並在INESC TEC的人工智慧與決策分析實驗室LIAAD進行研究。他目前擔任葡萄牙統計局的方法論服務主任，專長於統計學、數據科學、網絡挖掘和市場研究。他的一些研究貢獻涉及創新與就業、協作網絡和數據可視化。他擁有超過50篇出版物，包括專業期刊文章和書籍章節，並編輯了3本書籍。Pedro也是ISLP（國際統計素養計畫）的副主任。

Joaquim Margarido 是波爾圖高等工程學院（ISEP）的畢業生，擁有多代理系統的碩士學位。擁有IT專業知識的他，教授使用Java、Python、C#、SQL和網頁技術的程式設計。Joaquim致力於實用解決方案，為多家公司開發軟體，解決常見挑戰。他對創新軟體解決方案的承諾反映了他廣泛的訓練和在多種程式語言中的熟練程度，對教育和產業均有貢獻。

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